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使用大型语言模型进行可解释抑郁症评估
Jae-Joong Lee1, Jihoon Han1,2, Choong-Wan Woo1,2,3,4
1Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
PLOS digital health
|February 9, 2026
概括
这项研究引入了一种使用大型语言模型 (LLM) 来从对话中检测抑郁症的新方法. 可解释的LLM方法可以准确预测抑郁症的严重程度,改善临床应用.
科学领域:
- 计算语言学计算语言学
- 临床心理学 临床心理学
- 人工智能的人工智能是人工智能.
背景情况:
- 从对话文本中自动检测抑郁症正在获得吸引力.
- 使用大型语言模型 (LLM) 的现有方法缺乏可解释性,阻碍了临床使用.
- 解释性对于理解模型预测和确保临床有效性至关重要.
研究的目的:
- 开发一种新的,可解释的框架,用于使用LLMs进行自动抑郁症评估.
- 从对话文本中提取临床相关的,可解释的因素,表明抑郁症.
- 用这些可解释的因素和线性回归来预测抑郁症严重性得分.
主要方法:
- 利用LLM提示,从临床访谈成绩单中识别和提取可解释的与抑郁症相关的特征.
- 采用线性回归来根据提取的特征预测抑郁症严重程度得分.
- 评估了DAIC-WOZ基准数据集和独立的E-DAIC数据集的框架.
主要成果:
- 提出的方法在预测抑郁症严重程度 (PHQ-8分数) 方面取得了最先进的性能.
- 在DAIC-WOZ测试套件上获得了2.91的平均绝对误差 (MAE).
- 在独立的E-DAIC测试集上,证明了具有2.86的MAE的概括能力.
结论:
- 可解释的基于LLM的方法可以有效地识别抑郁症的关键行为和语言特征.
- 开发的框架显示了提高自动化抑郁症评估工具的临床实用性的重大前景.
- 这种方法弥合了LLM能力与临床决策透明度的需求之间的差距.
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